A new way to teach ethical thinking
As artificial intelligence (AI) becomes a bigger part of our daily lives, the question of how to teach AI ethics becomes increasingly important. How do we ensure that AI systems are fair, transparent, and accountable? And just as importantly, how do we ensure that the people working with AI think ethically in a constantly changing environment? What can I do to make it possible?
Traditional methods of teaching ethics, where instructors simply provide information to passive learners, are not designed to handle the complexities of AI ethics. This is where hetagogy, or self-determined learning, comes into play. Heutagogy puts the learner in the driver’s seat and allows them to shape their own learning process. This is especially valuable when dealing with the fast-paced and often ambiguous nature of AI ethics.
In this article, I want to explore how hetagogy can be an effective framework for teaching AI ethics. It’s more than just understanding ethical principles. It’s about developing critical thinking, adaptability, and a deeper sense of responsibility in the learners who will design and implement these powerful technologies.
What is hetagogy?
Hetagogy is a concept first introduced by Stewart Hase and Chris Kenyon in 2000. It goes beyond traditional pedagogy (teacher-directed) and andragogy (learner-centered) and takes learning to a new level: learner-driven learning. With this approach, students do more than just absorb information. They actively decide what and how they learn. They set their own learning goals, identify gaps in knowledge, and develop strategies to fill those gaps.
Rather than following a structured, linear curriculum, hetagogy allows for a more flexible, non-linear approach to learning. Learners are encouraged to explore different avenues, make connections, and most importantly, reflect on their learning process. This makes hetagogy especially relevant today, when technology, society, and ethics are all evolving rapidly.
For example, in AI ethics, where new ethical dilemmas are constantly emerging (think of AI’s role in surveillance, decision-making, and even content creation), students need to think critically and adapt their understanding of technology as It is necessary to develop the ability to The world and the changes around them.
AI ethics challenges
Teaching AI ethics is difficult. It’s not just a matter of learning a set of rules and principles. AI ethics is an area with many gray areas. What is considered ethical in one context may be problematic in another. For example, AI-powered facial recognition could be used to improve security in public spaces, but it could also raise serious privacy and surveillance concerns, especially in marginalized communities.
The complexity of AI ethics requires more than a textbook understanding of ethical theories such as deontology and consequentialism. Learners need to be able to apply these theories to specific, often uncertain, real-world situations. This is where hetagogy shines by encouraging students to take responsibility for their own learning, ask difficult questions, and explore different perspectives.
Why Heutagogy is effective for AI ethics
critical thinking and ethical reflection
AI ethics is not a subject that you can just memorize a few facts and call it a day. Deep critical thinking is required. You should be able to ask questions such as:
What are the possible benefits and risks of using this AI system? What impact might this technology have on different groups of people, especially those who are already marginalized? If the system fails, who is responsible and what are the consequences?
A hetagogical approach naturally encourages learners to engage with these types of questions at a deeper level. Rather than simply learning the “right” answer, students are taught to think about the big picture, explore ethical dilemmas, and reflect on their own understanding of what is at stake.
Self-directed and situated learning
AI ethics is not one-size-fits-all. The ethical implications of AI depend on the context in which the technology is used. For example, AI models used in healthcare have very different ethical challenges than those used on social media platforms.
Heutagogy supports self-directed learning by giving students the freedom to explore the ethical issues most relevant to their interests and particular field of work. Learners interested in AI for criminal justice may focus on predictive police ethics. At the same time, other researchers working in education may investigate how AI impacts privacy and fairness in online learning environments. This enables a richer, more personalized learning experience in which students engage deeply with ethical issues that are important to them.
Adaptability and lifelong learning
One of Hetagogy’s greatest strengths is that it encourages learners to become lifelong learners. This is essential in the world of AI, where technology and ethical considerations are constantly changing. What is ethical today may not be ethical tomorrow, and new challenges arise all the time.
In a self-determined learning environment, students do not stop learning just when the course is finished. They have the skills to keep asking questions, staying informed, and adapting to new ethical challenges as they arise. This adaptability is especially important in AI ethics, where new developments such as self-driving cars and AI-generated content can raise entirely new ethical questions.
Collaboration and ethical dialogue
Ethical thinking does not occur in isolation. In the real world, AI ethics requires collaboration between engineers, ethicists, policy makers, and, in some cases, the broader public. AI systems impact everyone, so it’s essential that conversations include diverse voices.
Heutagogy supports this collaborative learning approach. Learners in arbitrary environments often participate in peer-to-peer learning and group discussions rather than simply working on individual assignments. This reflects the collaborative process that is essential to addressing ethical challenges in AI. By working together, students learn to understand different perspectives, question their own assumptions, and reach more nuanced ethical conclusions.
Bringing hetagogy to AI ethics education
So how can we actually implement hetagogy when teaching AI ethics? Here are some strategies that can help.
Encourage students to create their own case studies
They can do it based on real-world AI technologies that interest them. Identify an ethical issue, explore its context, and present your findings using a variety of ethical frameworks. Use problem-based learning (PBL)
This is where learners solve real-world ethical dilemmas, such as algorithmic bias and privacy issues. This helps you practice applying ethical principles to complex real-life scenarios. Have students keep a reflection journal
They should regularly document their evolving understanding of AI ethics and the issues they are addressing. Facilitate group discussion
In this, learners can present different ethical perspectives, discuss pros and cons, and challenge each other to think more deeply about issues.
Embracing hetagogy allows learners to take control of their ethical education, think critically, adapt to new challenges, and collaborate with others to navigate the complex moral landscape of AI. This is an approach that teaches ethics and instills a sense of responsibility and lifelong learning, which are essential qualities for anyone working in AI today. Heutagogy doesn’t just help learners understand AI ethics. It helps them live it.
References: Blaschke, LM 2012. “Hetagogy and lifelong learning: A review of hetagogy practices and self-determined learning.” International Review of Research in Open and Distributed Learning 13 (1): 56–71. Hase, S., and C. Kenyon. 2000. “From Andragogy to Heutagogy”. UltiBASE article. Boddington, P. 2017. Toward a code of ethics for artificial intelligence. Springer.
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